A multi-modal optimization approach to single path planning for unmanned aerial vehicle

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


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  • University of Science and Technology of China, Hefei, China


In the past few years, Evolutionary Algorithms (EAs) based UAV path planners have drawn increasing research interests. However, they are not scalable to large-scale problems, i.e., lots of waypoints. Recently, we have proposed a novel EA-based framework, named Separately Evolving Waypoints (SEW), that can deal with large-scale problems. However, the difficulty of UAV path planning depends not only on the number of waypoints, but on the number of constraints it has to satisfy, especially the number of obstacles. In particular, the number of waypoints required is also partly determined by the number of constraints. Hence, it is critical to further improve SEW with respect to large number of obstacles. Originally, a state-of-the-art global optimization
approach is employed. In this work, we discuss how the increasing number of obstacles will deteriorate the performance of the global optimizer, then we propose multi-modal optimization approaches that facilitates the performance of SEW against large number of obstacles.


Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Congress on Evolutionary Computation
Publication statusPublished - 24 Jul 2016
Event2016 IEEE Congress on Evolutionary Computation (CEC) - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


Conference2016 IEEE Congress on Evolutionary Computation (CEC)
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